Relining the garbage can of organizational decision-making: modeling the arrival of problems and solutions as queues

The garbage can account of organizations where problems, solutions, and people chase each other is often invoked but rarely studied since its publication 44 years ago. It has been critiqued for being a metaphor rather than a model, and offering a deterministic rather than stochastic account. We reline the garbage can model of organizational decision-making by modeling the arrival of problems, people, and solutions as queues that get matched randomly. We show that queuing models allow us to understand the effect of using either experts, supervisor approval, teams, and deviation from supervision on problem resolution and oversight. Our approach shows that manager approval increased the standard deviation of problem resolution, whereas queues are processed faster and have lower variance when there is oversight or teamwork, or when manager approval is bypassed due to independent action by problem solvers. It also shows the costs of using an organizational hierarchy to address problems with different levels of difficulty, or specialization to address a mixture of fundamentally different problems. Thus, a stochastic garbage can model provides insights into why organizations make many decisions but often fail to resolve problems!